class_docstr: MNIST data set from http://yann.lecun.com/exdb/mnist/
  
  Normalize defaults to true and scales the data 0 to 1.
  Size defaults to 28 for 28x28 pixels, specifying a smaller values allows
  cropping to a smaller size.
code: def __init__(
      self,
      path=".",
      subset_pct=100,
      normalize=True,
      sym_range=False,
      size=28,
      shuffle=False,
  ):
      super(MNIST, self).__init__(
          "mnist.pkl.gz",
          "https://s3.amazonaws.com/img-datasets",
          15296311,
          path=path,
      )
      self.normalize = normalize
      self.sym_range = sym_range
      self.size = size
      self.shuffle = shuffle
      self.subset_pct = subset_pct
  
imports: from __future__ import division
  from future import standard_library
  from builtins import str
  import gzip
  import logging
  import numpy
  import os
  import tarfile
  from neon.util.compat import pickle
  from neon.util.compat import pickle_load
  from neon.data.datasets import Dataset
  from neon.data.dataiterator import ArrayIterator
label_desc: Use image node_id 1 to create a new MNIST object from inherited base classes: Dataset with example: obj = MNIST(path, subset_pct, normalize, sym_range, size, shuffle)